Hi
Is there a way to use backslash operator (\) for linear systems of equation when you cannot store the matrix in memory. Like for example if A is dense 8500*85000 matrix which cannot be stored in the memory and A\b is to computed, where b is column vector of appropriate dimensions then which would be the most efficient out of core algorithm (external memory where parts of the matrix are stored on a hard drive) to do it on a single desktop in a reasonable amount of time. I know of parallel solutions based on LU decomposition of matrix or Cholesky decomposition for positive definite matrix which are based on block decomposition of matrices which however requires block cyclical distribution of matrix (depending on the number of nodes available) and thereafter Scalapack can be used to obtain the solution.
• The problem for which is to be done requires running of optimization routines for optimizing Restricted Maximum Likelihood function for 42 parameters and generate CO2 flux estimates for north America through inverse modeling
Any ideas and suggestion would be greatly helpful

"Vineet " <yadavvineet@gmail.com> wrote in message
news:h8o79m$28p$1@fred.mathworks.com...
> Hi
> Is there a way to use backslash operator (\) for linear systems of
> equation when you cannot store the matrix in memory. Like for example if
> A is dense 8500*85000 matrix which cannot be stored in the memory and A\b
> is to computed, where b is column vector of appropriate dimensions then
> which would be the most efficient out of core algorithm (external memory
> where parts of the matrix are stored on a hard drive) to do it on a single
> desktop in a reasonable amount of time. I know of parallel solutions
> based on LU decomposition of matrix or Cholesky decomposition for positive
> definite matrix which are based on block decomposition of matrices which
> however requires block cyclical distribution of matrix (depending on the
> number of nodes available) and thereafter Scalapack can be used to obtain
> the solution.
> • The problem for which is to be done requires running of
> optimization routines for optimizing Restricted Maximum Likelihood
> function for 42 parameters and generate CO2 flux estimates for north
> America through inverse modeling
> Any ideas and suggestion would be greatly helpful

You could use codistributed arrays, which are part of Parallel Computing
Toolbox:

or if you know how to compute the product of the matrix with a vector
without actually loading/creating the matrix, you could use one of the
iterative solvers (PCG, GMRES, etc.) listed in HELP SPARFUN with a function
handle that computes an appropriate matrix/vector products. For instance,
for the tridiagonal matrix:

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